机器学习分析拉曼光谱 量化复杂有机矿物混合物的有机成分
Mahsa Zarei1, Natalia V Solomatova2, Hoda Aghaei2
1Department of Chemistry, The University of British Columbia, 2036 Main Mall, Vancouver, British Columbia V6T 1Z1, Canada.
Analytical chemistry
|September 12, 2023
概括
拉曼光谱可以分析土壤成分,但光和复杂的混合物带来了挑战. 卷积神经网络 (CNN) 和部分最小平方回归 (PLSR) 模型都表现出强的性能,CNN在低信号对噪声场景中表现出色.
科学领域:
- 农业科学 农业科学
- 分析化学 分析化学
- 频谱学是一种光谱学.
背景情况:
- 土壤的化学成分对农业政策和实践至关重要.
- 拉曼光谱技术提供了对有机物质的快速,非侵入性分析,但由于复杂的组成和光,在土壤中面临着挑战.
- 克服光谱干扰是准确土壤分析的关键.
研究的目的:
- 为了比较卷积神经网络 (CNN) 和部分最小平方回归 (PLSR) 模型的性能,用于分析土壤中的氨基酸组成.
- 评估光谱采样和数据预处理对模型准确性的影响.
- 确定矿物质和光干扰土壤拉曼光谱的最佳建模策略.
主要方法:
- 创建了模仿土壤的氨基酸混合物,光托尼特粘土和矿物成分.
- 应用并将CNN和PLSR多变量模型与拉曼光谱数据进行比较.
- 使用体积平均光谱采样和数据预处理技术.
主要成果:
- 无论是PLSR还是CNN模型都表现出高的预测准确性 (PLSR的R平方值高达0.98,CNN的0.97).
- 性能受到矩阵复杂性和信号噪声比的影响.
- 在有机信号噪声比率非常低的场景中,CNN模型的表现优于PLSR.
结论:
- 量平均取样和数据预处理显著提高了对土壤的拉曼分析.
- PLSR和CNN模型为土壤有机物量化提供了可比,强大的性能.
- CNN模型为分析具有低有机信号对噪声的复杂土壤样本提供了优势,这对于准确的农业应用至关重要.
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